Research Scientist, Frontier Red Team (Cyber)

Posted 6 Hours Ago
Be an Early Applicant
2 Locations
280K-425K Annually
Mid level
Artificial Intelligence • Natural Language Processing • Generative AI
The Role
As a Research Scientist in the Cyber workstream, you will design and analyze experiments focusing on large language models in cybersecurity. Responsibilities include leading technical discussions, collaborating on evaluations, and working with external partners to assess model implications on cybersecurity. A strong background in AI/ML and offensive cybersecurity is desirable.
Summary Generated by Built In
About Anthropic

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About Anthropic

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

We’re building a team that will research and mitigate extreme risks from future models.

This team will intensively red-team models to test the most significant risks they might be capable of in the area of Cybersecurity. We believe that clear demonstrations can significantly advance technical research and mitigations, as well as identify effective policy interventions to promote and incentivize safety.

As part of this team, you will lead research to baseline current models and test whether future frontier capabilities could cause significant harm. Day-to-day, you may decide you need to finetune a model to see whether it becomes superhuman in an eval you’ve designed; whiteboard a threat model with a national security expert; test a new training procedure or how a model uses a tool; or brief government, labs, and other research teams. Our goal is to see the frontier before we get there.

We’re currently hiring for our Cyber workstream (as outlined in our Responsible Scaling Policy). By nature, this team will be an unusual combination of backgrounds. We are particularly looking for people with experience in these domains:

Cybersecurity: You’re a white hat hacker who is curious about how LLMs might be able to do some or all of your work. You’re an academic who researches RL for cybersecurity. You’ve participated in or built CTF competitions and cyber ranges, and you want to automate them.

Evaluations: You’ve managed a large-scale benchmark development project, in AI or other domains. You have ideas about how AI and ML evaluations can be more realistic. You’ve thought hard about all the ways to improve model performance on a given benchmark.

Do not rule yourself out if you do not fit one of those categories - it’s plausible the people we’re looking for do not fit any of the above! If you think about the most significant upsides and downsides of AI, and you can do good research to get glimpses of what those look like, please consider applying

Responsibilities

  • Design, run, and analyze scientific experiments to advance our understanding of large language models and their application to cybersecurity tasks
  • Lead technical design discussions to ensure our infrastructure can support both current needs and future research directions
  • Collaborate with other engineers to maintain our evaluations codebase
  • Work with external partners to develop novel evaluations to accurately assess the cybersecurity implications of our models
  • Partner closely with researchers, data scientists, policy experts, and other cross-functional partners to advance Anthropic’s safety mission

You may be a good fit if you

  • Have strong software engineering or AI/ML research experience, and strong interest or experience in offensive cybersecurity
  • Have a strong interest in societal and policy impacts of AI
  • Take pride in writing clean, well-documented code in Python that others can build upon
  • Have a track record of using technical infrastructure to interface effectively with machine learning models
  • Have familiarity with prompting and engineering large language models
  • Are able to balance research goals with practical engineering constraints
  • Have strong problem-solving skills and a results-oriented mindset
  • Have excellent communication skills and ability to work in a collaborative environment
  • Prefer fast-moving collaborative projects to extensive solo efforts

Strong candidates may also have experience with

  • Training, scaffolding, or evaluating models for cyber capabilities
  • Competition CTF challenges
  • Professional cyber pentesting
  • Building sophisticated and realistic cyber range environments
  • Representative projects applying large language models or machine learning to cybersecurity tasks
  • Developing evaluations or benchmarks for large language models

Candidates need not have

  • Previous professional experience in AI safety
  • 100% of the skills needed to perform the job


The expected salary range for this position is:

Annual Salary:

$280,000$425,000 USD

Logistics

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.
Location-based hybrid policy:
Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.

Top Skills

Python
The Company
57 Employees
Remote Workplace

What We Do

Anthropic is an AI safety and research company that’s working to build reliable, interpretable, and steerable AI systems. Our research interests span multiple areas including natural language, human feedback, scaling laws, reinforcement learning, code generation, and interpretability.

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